Images captured by an image capture device may suffer from degradation of the image quality which may be caused by defects of the image sensor itself, defects arising from integration of the captured light in the sensor, or forms of degradation such as motion blur, which are due to elements external to the sensor.
Motion blur is a form of degradation which may affect a captured image if the sighting axis is moving at the moment when the image is captured. This degradation may be seen in the image in the form of a visible image motion which causes a blurred area to appear in a captured scene. Thus the movement blur generally originates in a motion of the sighting axis of the capture device during the image capture.
If a camera is carried by a moving support, such as a person who is walking or even stationary, or a vehicle, the captured images may show a motion blur in the form of visual effects that degrade the image quality. The captured image which is affected by the motion blur can be represented by a convolution product between a point spread function (PSF) associated with the motion blur and the captured original scene O within the captured image. Consequently there are known methods of correcting a captured image affected by motion blur, based on a deconvolution of the captured image with a point spread function which can represent the motion blur that has affected the captured image. More precisely, this deconvolution operation corresponds to the convolution product between the captured image and the point spread function associated with the motion blur. This deconvolution can be implemented, for example, by applying a filter (a Fourier-based Wiener filter, or other algorithms using other bases, as described in ‘A Wavelet Tour of Signal Processing’ by Stéphane Mallat.
However, in order to apply these methods, it is desirable to determine a motion blur shape which has affected the image to be corrected, for the purpose of defining the point spread function associated with it. A deconvolution operation based on this associated function can then be carried out.
The implementation of a procedure of this kind may require complex calculations. It is also desirable to base this procedure of determining the point spread function associated with a motion blur on certain assumptions which may subsequently prove to be either more or less appropriate and which may therefore result in an unsatisfactory correction of the captured image.
Image capture systems are adapted to reduce these visual effects. In some systems, this is done by stabilizing the sighting axis by incorporating a mechanical inertial stabilization module in the camera or by mounting the camera on a mechanical module such as a platform which provides inertial stabilization. The mechanisms of these systems can be controlled according to information obtained from gyroscopes. They can therefore be used to stabilize the sighting axis of a camera in an inertial frame of reference. In these conditions it becomes possible to capture images completely independently of any irregular movements affecting the support of the camera which is used. This image capture system based on an inertial frame of reference provides an effective way of avoiding the visual effects, that is to say the movement blur, caused by irregular movements of the camera support.
Other types of image capture systems can incorporate a digital stabilization module designed to stabilize the captured images by digitally processing them.
However, even if the motion blur is reduced by an inertial control of the motion of the sighting axis, there may be some disturbance in the captured images, due to fixed spatial noise.
The term “spatial noise” denotes an undesirable difference (or offset) between the values of pixels of a single image which should theoretically be equal when the pixels correspond to the same input signal. This type of degradation may be due to the intrinsic properties of the capture device concerned.
A digital image is represented by a matrix of pixels, each corresponding to a color level or a gray level of the image. An image corresponds to a scene captured by a matrix of flux sensors, the light energy received by these sensors being converted subsequently into corresponding electrical signals. The different sensors in the sensor matrix are usually theoretically identical, but in practice there are differences between them which lead to undesirable offsets, as defined above, between the different pixels of a captured image of a scene corresponding to an incoming flux which is identical at every point of the captured image, thus giving rise to spatial noise in this image.
This spatial noise can be corrected by capturing an image of a black body and calculating the undesirable offsets between the different pixels captured by the flux sensors of the matrix, so that these offsets can be corrected in the next images captured. However, this form of correction requires an interruption in the image capture process.
Since the offsets between the values of the pixels of a single image due to spatial noise may vary over time, as a function of temperature for example, it may be useful to repeat the procedure of correcting these offsets regularly in the course of the image capture, without interrupting the image capture operation.
In an image capture system with inertial stabilization, spatial noise creates a troublesome disturbance, because disturbances due to the motion of the sighting axis are prevented. This makes it even more desirable to be able to correct the spatial noise in this context.
It should therefore be noted that, on the one hand, captured images may be affected by motion blur in an image capture system in which the motion of the sighting axis is not inertially stabilized, while, on the other hand, captured images may be affected by disturbances due to spatial noise in an image capture system in which the motion of the sighting axis is inertially stabilized.